{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TimeEval parameter optimization result analysis of extra experiments\n",
    "\n",
    "Extra experiments and their reason:\n",
    "\n",
    "- PST: redo experiment with fixed parameter search space\n",
    "- NumentaHTM: also optimize (previously) fixed parameters, because of bad performance\n",
    "- ARIMA and DeepNAP: Were included in the algorithm set that should be optimized manually, but had very large runtimes. We decied to test some parameter ranges automatically instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import json\n",
    "import warnings\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pathlib import Path\n",
    "from timeeval import Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define data and results folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available result directories:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/projects/akita/results/2021-10-11_optim-part4'),\n",
       " PosixPath('/home/projects/akita/results/2021-09-27_shared-optim'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-06_optim-part1'),\n",
       " PosixPath('/home/projects/akita/results/2021-09-27_default-params-1&2&3&4-merged'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-14_optim-extra'),\n",
       " PosixPath('/home/projects/akita/results/.ipynb_checkpoints'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-08_optim-part3'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-07_optim-part2'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-17_optim-extra2'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-12_optim-part5'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-12_optim-part6')]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Selecting:\n",
      "Data path: /home/projects/akita/data/test-cases\n",
      "Result path: /home/projects/akita/results/2021-10-14_optim-extra\n"
     ]
    }
   ],
   "source": [
    "# constants and configuration\n",
    "data_path = Path(\"/home/projects/akita/data\") / \"test-cases\"\n",
    "result_root_path = Path(\"/home/projects/akita/results\")\n",
    "experiment_result_folder = \"2021-10-14_optim-extra\"\n",
    "\n",
    "# build paths\n",
    "result_paths = [d for d in result_root_path.iterdir() if d.is_dir()]\n",
    "print(\"Available result directories:\")\n",
    "display(result_paths)\n",
    "\n",
    "result_path = result_root_path / experiment_result_folder\n",
    "print(\"\\nSelecting:\")\n",
    "print(f\"Data path: {data_path.resolve()}\")\n",
    "print(f\"Result path: {result_path.resolve()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load results and dataset metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading results from /home/projects/akita/results/2021-10-14_optim-extra\n"
     ]
    }
   ],
   "source": [
    "# load results\n",
    "print(f\"Reading results from {result_path.resolve()}\")\n",
    "df = pd.read_csv(result_path / \"results.csv\")\n",
    "\n",
    "# add dataset_name column\n",
    "df[\"dataset_name\"] = df[\"dataset\"].str.split(\".\").str[0]\n",
    "\n",
    "# load dataset metadata\n",
    "dmgr = Datasets(data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove wrongly configured experiments:\n",
    "\n",
    "```\n",
    "    algorithms.append(\n",
    "        numenta_htm(params=FullParameterGrid({\n",
    "            \"n_trees\": [10, 100, 200],\n",
    "            \"n_estimators\": [10, 100, 200],\n",
    "            \"bootstrap\": [True, False]\n",
    "        }))\n",
    "    )\n",
    "```\n",
    " This parameter grid should have been applied to the _Random Black Forest (RR)_ algorithm. The executions on NumentaHTM are not meaningful!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_indices = (df[\"algorithm\"] == \"NumentaHTM\") & (df[\"hyper_params\"].apply(lambda x: any(v in x for v in [\"n_estimators\", \"n_trees\", \"boostrap\"])))\n",
    "df = df[~drop_indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract target optimized parameter names that were iterated in this run (per algorithm):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA ['differencing_degree']\n",
      "DeepNAP ['partial_sequence_length', 'lstm_layers', 'rnn_hidden_size', 'linear_hidden_size']\n",
      "PST ['sim']\n",
      "NumentaHTM ['globalDecay', 'maxSynapsesPerSegment', 'maxSegmentsPerCell', 'potentialPct', 'alpha', 'synPermInactiveDec', 'initialPerm', 'columnCount', 'autoDetectWaitRecords', 'numActiveColumnsPerInhArea', 'encoding_output_width', 'pamLength', 'permanenceInc', 'encoding_input_width', 'synPermActiveInc', 'inputWidth', 'maxAge', 'synPermConnected', 'newSynapseCount', 'permanenceDec', 'cellsPerColumn', 'minThreshold', 'activationThreshold']\n"
     ]
    }
   ],
   "source": [
    "algo_param_mapping = {}\n",
    "algorithms = df[\"algorithm\"].unique()\n",
    "param_ignore_list = [\"max_anomaly_window_size\", \"anomaly_window_size\", \"neighbourhood_size\", \"window_size\", \"n_init_train\", \"embed_dim_range\"]\n",
    "\n",
    "for algo in algorithms:\n",
    "    param_sets = df.loc[df[\"algorithm\"] == algo, \"hyper_params\"].unique()\n",
    "    param_sets = [json.loads(ps) for ps in param_sets]\n",
    "    param_names = np.unique([name for ps in param_sets for name in ps if name not in param_ignore_list])\n",
    "    search_space = set()\n",
    "    for param_name in param_names:\n",
    "        values = []\n",
    "        for ps in param_sets:\n",
    "            try:\n",
    "                values.append(ps[param_name])\n",
    "            except:\n",
    "                pass\n",
    "        values = np.unique(values)\n",
    "        if values.shape[0] > 1:\n",
    "            search_space.add(param_name)\n",
    "    algo_param_mapping[algo] = list(search_space)\n",
    "\n",
    "for algo in algo_param_mapping:\n",
    "    print(algo, algo_param_mapping[algo])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract optimized parameters and their values (columns: optim_param_name and optim_param_value) for each experiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_hyper_params(algo):\n",
    "    param_names = algo_param_mapping[algo]\n",
    "    def extract(value):\n",
    "        params = json.loads(value)\n",
    "        result = None\n",
    "        for name in param_names:\n",
    "            try:\n",
    "                value = params[name]\n",
    "                result = pd.Series([name, value], index=[\"optim_param_name\", \"optim_param_value\"])\n",
    "                break\n",
    "            except KeyError:\n",
    "                pass\n",
    "        if result is None:\n",
    "            return pd.Series([np.nan, np.nan], index=[\"optim_param_name\", \"optim_param_value\"])\n",
    "        return result\n",
    "    return extract\n",
    "\n",
    "df[[\"optim_param_name\", \"optim_param_value\"]] = \"\"\n",
    "for algo in algo_param_mapping:\n",
    "    df_algo = df.loc[df[\"algorithm\"] == algo]\n",
    "    df.loc[df_algo.index, [\"optim_param_name\", \"optim_param_value\"]] = df_algo[\"hyper_params\"].apply(extract_hyper_params(algo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract window size parameters (dependent params) and convert them into multiples of the dataset period size:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_scores_df(algorithm_name, dataset_id, optim_params, repetition=1):\n",
    "    params_id = df.loc[(df[\"algorithm\"] == algorithm_name) & (df[\"collection\"] == dataset_id[0]) & (df[\"dataset\"] == dataset_id[1]) & (df[\"optim_param_name\"] == optim_params[0]) & (df[\"optim_param_value\"] == optim_params[1]), \"hyper_params_id\"].item()\n",
    "    path = (\n",
    "        result_path /\n",
    "        algorithm_name /\n",
    "        params_id /\n",
    "        dataset_id[0] /\n",
    "        dataset_id[1] /\n",
    "        str(repetition) /\n",
    "        \"anomaly_scores.ts\"\n",
    "    )\n",
    "    return pd.read_csv(path, header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define plotting functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_use_plotly = True\n",
    "try:\n",
    "    import plotly.offline\n",
    "except ImportError:\n",
    "    default_use_plotly = False\n",
    "\n",
    "def plot_scores(algorithm_name, dataset_name, use_plotly: bool = default_use_plotly, **kwargs):\n",
    "    if isinstance(algorithm_name, tuple):\n",
    "        algorithms = [algorithm_name]\n",
    "    elif not isinstance(algorithm_name, list):\n",
    "        raise ValueError(\"Please supply a tuple (algorithm_name, optim_param_name, optim_param_value) or a list thereof as first argument!\")\n",
    "    else:\n",
    "        algorithms = algorithm_name\n",
    "    # construct dataset ID\n",
    "    dataset_id = (\"GutenTAG\", f\"{dataset_name}.unsupervised\")\n",
    "\n",
    "    # load dataset details\n",
    "    df_dataset = dmgr.get_dataset_df(dataset_id)\n",
    "\n",
    "    # check if dataset is multivariate\n",
    "    dataset_dim = df.loc[df[\"dataset_name\"] == dataset_name, \"dataset_input_dimensionality\"].unique().item()\n",
    "    dataset_dim = dataset_dim.lower()\n",
    "    \n",
    "    auroc = {}\n",
    "    df_scores = pd.DataFrame(index=df_dataset.index)\n",
    "    skip_algos = []\n",
    "    algos = []\n",
    "    for algo, optim_param_name, optim_param_value in algorithms:\n",
    "        optim_params = f\"{optim_param_name}={optim_param_value}\"\n",
    "        algos.append((algo, optim_params))\n",
    "        # get algorithm metric results\n",
    "        try:\n",
    "            auroc[(algo, optim_params)] = df.loc[\n",
    "                (df[\"algorithm\"] == algo) & (df[\"dataset_name\"] == dataset_name) & (df[\"optim_param_name\"] == optim_param_name) & (df[\"optim_param_value\"] == optim_param_value),\n",
    "                \"ROC_AUC\"\n",
    "            ].item()\n",
    "        except ValueError:\n",
    "            warnings.warn(f\"No ROC_AUC score found! Probably {algo} with params {optim_params} was not executed on {dataset_name}.\")\n",
    "            auroc[(algo, optim_params)] = -1\n",
    "            skip_algos.append((algo, optim_params))\n",
    "            continue\n",
    "\n",
    "        # load scores\n",
    "        training_type = df.loc[df[\"algorithm\"] == algo, \"algo_training_type\"].values[0].lower().replace(\"_\", \"-\")\n",
    "        try:\n",
    "            df_scores[(algo, optim_params)] = load_scores_df(algo, (\"GutenTAG\", f\"{dataset_name}.{training_type}\"), (optim_param_name, optim_param_value)).iloc[:, 0]\n",
    "        except (ValueError, FileNotFoundError):\n",
    "            warnings.warn(f\"No anomaly scores found! Probably {algo} was not executed on {dataset_name} with params {optim_params}.\")\n",
    "            df_scores[(algo, optim_params)] = np.nan\n",
    "            skip_algos.append((algo, optim_params))\n",
    "    algorithms = [a for a in algos if a not in skip_algos]\n",
    "\n",
    "    if use_plotly:\n",
    "        return plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs)\n",
    "    else:\n",
    "        return plot_scores_plt(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs)\n",
    "\n",
    "def plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs):\n",
    "    import plotly.offline as py\n",
    "    import plotly.graph_objects as go\n",
    "    import plotly.figure_factory as ff\n",
    "    import plotly.express as px\n",
    "    from plotly.subplots import make_subplots\n",
    "\n",
    "    # Create plot\n",
    "    fig = make_subplots(2, 1)\n",
    "    if dataset_dim == \"multivariate\":\n",
    "        for i in range(1, df_dataset.shape[1]-1):\n",
    "            fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, i], name=f\"channel-{i}\"), 1, 1)\n",
    "    else:\n",
    "        fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, 1], name=\"timeseries\"), 1, 1)\n",
    "    fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset[\"is_anomaly\"], name=\"label\"), 2, 1)\n",
    "    \n",
    "    for item in algorithms:\n",
    "        algo, optim_params = item\n",
    "        fig.add_trace(go.Scatter(x=df_scores.index, y=df_scores[item], name=f\"{algo}={auroc[item]:.4f} ({optim_params})\"), 2, 1)\n",
    "    fig.update_xaxes(matches=\"x\")\n",
    "    fig.update_layout(\n",
    "        title=f\"Results of {','.join(np.unique([a for a, _ in algorithms]))} on {dataset_name}\",\n",
    "        height=400\n",
    "    )\n",
    "    return py.iplot(fig)\n",
    "\n",
    "def plot_scores_plt(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs):\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    # Create plot\n",
    "    fig, axs = plt.subplots(2, 1, sharex=True, figsize=(20, 8))\n",
    "    if dataset_dim == \"multivariate\":\n",
    "        for i in range(1, df_dataset.shape[1]-1):\n",
    "            axs[0].plot(df_dataset.index, df_dataset.iloc[:, i], label=f\"channel-{i}\")\n",
    "    else:\n",
    "        axs[0].plot(df_dataset.index, df_dataset.iloc[:, 1], label=f\"timeseries\")\n",
    "    axs[1].plot(df_dataset.index, df_dataset[\"is_anomaly\"], label=\"label\")\n",
    "    \n",
    "    for item in algorithms:\n",
    "        algo, optim_params = item\n",
    "        axs[1].plot(df_scores.index, df_scores[item], label=f\"{algo}={auroc[item]:.4f} ({optim_params})\")\n",
    "    axs[0].legend()\n",
    "    axs[1].legend()\n",
    "    fig.suptitle(f\"Results of {','.join(np.unique([a for a, _ in algorithms]))} on {dataset_name}\")\n",
    "    fig.tight_layout()\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">PR_AUC</th>\n",
       "      <th colspan=\"3\" halign=\"left\">ROC_AUC</th>\n",
       "      <th>train_main_time</th>\n",
       "      <th>execute_main_time</th>\n",
       "      <th>repetition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>algorithm</th>\n",
       "      <th>optim_param_name</th>\n",
       "      <th>optim_param_value</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PST</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">sim</th>\n",
       "      <th>simn</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.338418</td>\n",
       "      <td>0.168211</td>\n",
       "      <td>0.016049</td>\n",
       "      <td>0.796201</td>\n",
       "      <td>0.858833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.581874</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>simo</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.339220</td>\n",
       "      <td>0.168737</td>\n",
       "      <td>0.016107</td>\n",
       "      <td>0.793667</td>\n",
       "      <td>0.860587</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.927654</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"69\" valign=\"top\">NumentaHTM</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">synPermInactiveDec</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.100046</td>\n",
       "      <td>0.046329</td>\n",
       "      <td>0.484697</td>\n",
       "      <td>0.568568</td>\n",
       "      <td>0.505253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.840149</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.005</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.031569</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.001</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097582</td>\n",
       "      <td>0.030826</td>\n",
       "      <td>0.490909</td>\n",
       "      <td>0.562129</td>\n",
       "      <td>0.499016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.519050</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">synPermConnected</th>\n",
       "      <th>0.1</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.028571</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.100681</td>\n",
       "      <td>0.032129</td>\n",
       "      <td>0.488384</td>\n",
       "      <td>0.564063</td>\n",
       "      <td>0.502727</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.036952</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">synPermActiveInc</th>\n",
       "      <th>0.05</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.103190</td>\n",
       "      <td>0.036100</td>\n",
       "      <td>0.487967</td>\n",
       "      <td>0.566906</td>\n",
       "      <td>0.505730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.191249</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.382094</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">potentialPct</th>\n",
       "      <th>0.1</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.076253</td>\n",
       "      <td>0.036857</td>\n",
       "      <td>0.475354</td>\n",
       "      <td>0.575691</td>\n",
       "      <td>0.511529</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.048296</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.349593</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.079891</td>\n",
       "      <td>0.012154</td>\n",
       "      <td>0.487576</td>\n",
       "      <td>0.542880</td>\n",
       "      <td>0.499848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.564084</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">permanenceInc</th>\n",
       "      <th>0.05</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.100419</td>\n",
       "      <td>0.034597</td>\n",
       "      <td>0.489518</td>\n",
       "      <td>0.565133</td>\n",
       "      <td>0.503770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.821784</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.729472</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.095074</td>\n",
       "      <td>0.037637</td>\n",
       "      <td>0.488905</td>\n",
       "      <td>0.563908</td>\n",
       "      <td>0.503042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.432688</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">permanenceDec</th>\n",
       "      <th>0.1</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.088535</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.096756</td>\n",
       "      <td>0.037384</td>\n",
       "      <td>0.480306</td>\n",
       "      <td>0.564283</td>\n",
       "      <td>0.502494</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.988004</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.096898</td>\n",
       "      <td>0.037147</td>\n",
       "      <td>0.489061</td>\n",
       "      <td>0.564216</td>\n",
       "      <td>0.502026</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.435965</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">pamLength</th>\n",
       "      <th>1</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.426819</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.095681</td>\n",
       "      <td>0.033515</td>\n",
       "      <td>0.488368</td>\n",
       "      <td>0.564174</td>\n",
       "      <td>0.502260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.228420</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097651</td>\n",
       "      <td>0.035734</td>\n",
       "      <td>0.487602</td>\n",
       "      <td>0.564159</td>\n",
       "      <td>0.505470</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.492750</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">numActiveColumnsPerInhArea</th>\n",
       "      <th>50.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.101024</td>\n",
       "      <td>0.046308</td>\n",
       "      <td>0.488905</td>\n",
       "      <td>0.569889</td>\n",
       "      <td>0.507470</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101.529713</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.106062</td>\n",
       "      <td>0.043342</td>\n",
       "      <td>0.487755</td>\n",
       "      <td>0.566368</td>\n",
       "      <td>0.505715</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.781598</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.968474</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">newSynapseCount</th>\n",
       "      <th>15.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097521</td>\n",
       "      <td>0.044778</td>\n",
       "      <td>0.481869</td>\n",
       "      <td>0.567878</td>\n",
       "      <td>0.507638</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.198422</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.814221</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.096892</td>\n",
       "      <td>0.034156</td>\n",
       "      <td>0.489061</td>\n",
       "      <td>0.563534</td>\n",
       "      <td>0.500584</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.618047</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">minThreshold</th>\n",
       "      <th>9.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.170242</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097300</td>\n",
       "      <td>0.034156</td>\n",
       "      <td>0.487347</td>\n",
       "      <td>0.564289</td>\n",
       "      <td>0.503147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.513560</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.095512</td>\n",
       "      <td>0.038428</td>\n",
       "      <td>0.485051</td>\n",
       "      <td>0.564175</td>\n",
       "      <td>0.502494</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.310443</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">maxSynapsesPerSegment</th>\n",
       "      <th>32.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.858673</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097227</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564318</td>\n",
       "      <td>0.502850</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.596435</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">maxSegmentsPerCell</th>\n",
       "      <th>64.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.673058</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.805896</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.019019</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">maxAge</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.659237</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">inputWidth</th>\n",
       "      <th>1024.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.791690</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.808960</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4096.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.313934</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">initialPerm</th>\n",
       "      <th>0.15</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.098333</td>\n",
       "      <td>0.035506</td>\n",
       "      <td>0.489193</td>\n",
       "      <td>0.564573</td>\n",
       "      <td>0.505337</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.422149</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.21</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.292459</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.094185</td>\n",
       "      <td>0.033943</td>\n",
       "      <td>0.488853</td>\n",
       "      <td>0.563277</td>\n",
       "      <td>0.501770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.953588</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">globalDecay</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.618928</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">encoding_output_width</th>\n",
       "      <th>75.0</th>\n",
       "      <td>0.001772</td>\n",
       "      <td>0.103327</td>\n",
       "      <td>0.048797</td>\n",
       "      <td>0.460202</td>\n",
       "      <td>0.590365</td>\n",
       "      <td>0.523820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.298736</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.496017</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.0</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.054558</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.498232</td>\n",
       "      <td>0.507109</td>\n",
       "      <td>0.499840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.232022</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">encoding_input_width</th>\n",
       "      <th>21.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.511325</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">columnCount</th>\n",
       "      <th>4096.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.107261</td>\n",
       "      <td>0.042018</td>\n",
       "      <td>0.487828</td>\n",
       "      <td>0.572741</td>\n",
       "      <td>0.505458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>138.440881</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.108738</td>\n",
       "      <td>0.048901</td>\n",
       "      <td>0.485606</td>\n",
       "      <td>0.567435</td>\n",
       "      <td>0.505431</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.900552</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.728706</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">cellsPerColumn</th>\n",
       "      <th>64.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097537</td>\n",
       "      <td>0.038532</td>\n",
       "      <td>0.483214</td>\n",
       "      <td>0.564593</td>\n",
       "      <td>0.504269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125.271610</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097297</td>\n",
       "      <td>0.036048</td>\n",
       "      <td>0.482959</td>\n",
       "      <td>0.564435</td>\n",
       "      <td>0.504269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.976719</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.415208</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">autoDetectWaitRecords</th>\n",
       "      <th>25.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.161906</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.497845</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.068992</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">alpha</th>\n",
       "      <th>0.2</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.821727</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.642060</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.947527</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">activationThreshold</th>\n",
       "      <th>12.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.085590</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.090262</td>\n",
       "      <td>0.033301</td>\n",
       "      <td>0.489374</td>\n",
       "      <td>0.559400</td>\n",
       "      <td>0.500006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.112704</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24.0</th>\n",
       "      <td>0.008143</td>\n",
       "      <td>0.408512</td>\n",
       "      <td>0.500058</td>\n",
       "      <td>0.050546</td>\n",
       "      <td>0.497997</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.860859</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">DeepNAP</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">rnn_hidden_size</th>\n",
       "      <th>200</th>\n",
       "      <td>0.000756</td>\n",
       "      <td>0.237885</td>\n",
       "      <td>0.006221</td>\n",
       "      <td>0.343013</td>\n",
       "      <td>0.721780</td>\n",
       "      <td>0.920811</td>\n",
       "      <td>14387.459396</td>\n",
       "      <td>552.284223</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0.000756</td>\n",
       "      <td>0.159084</td>\n",
       "      <td>0.013636</td>\n",
       "      <td>0.342973</td>\n",
       "      <td>0.721554</td>\n",
       "      <td>0.798259</td>\n",
       "      <td>14049.248946</td>\n",
       "      <td>1088.230355</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>0.000756</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.006143</td>\n",
       "      <td>0.342973</td>\n",
       "      <td>0.721554</td>\n",
       "      <td>0.919830</td>\n",
       "      <td>14134.171681</td>\n",
       "      <td>563.656898</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">partial_sequence_length</th>\n",
       "      <th>10</th>\n",
       "      <td>0.000766</td>\n",
       "      <td>0.255736</td>\n",
       "      <td>0.006954</td>\n",
       "      <td>0.347978</td>\n",
       "      <td>0.754539</td>\n",
       "      <td>0.930871</td>\n",
       "      <td>14400.246819</td>\n",
       "      <td>559.587427</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.474652</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.078508</td>\n",
       "      <td>0.744835</td>\n",
       "      <td>0.999840</td>\n",
       "      <td>6917.062416</td>\n",
       "      <td>208.561622</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000762</td>\n",
       "      <td>0.234055</td>\n",
       "      <td>0.006361</td>\n",
       "      <td>0.344595</td>\n",
       "      <td>0.733564</td>\n",
       "      <td>0.922823</td>\n",
       "      <td>14411.838211</td>\n",
       "      <td>640.338980</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000754</td>\n",
       "      <td>0.328702</td>\n",
       "      <td>0.006221</td>\n",
       "      <td>0.343013</td>\n",
       "      <td>0.722018</td>\n",
       "      <td>0.920811</td>\n",
       "      <td>14411.976403</td>\n",
       "      <td>554.589953</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">lstm_layers</th>\n",
       "      <th>3</th>\n",
       "      <td>0.000755</td>\n",
       "      <td>0.296958</td>\n",
       "      <td>0.098311</td>\n",
       "      <td>0.342923</td>\n",
       "      <td>0.672240</td>\n",
       "      <td>0.673023</td>\n",
       "      <td>14411.467708</td>\n",
       "      <td>1228.321475</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000755</td>\n",
       "      <td>0.116156</td>\n",
       "      <td>0.024373</td>\n",
       "      <td>0.000532</td>\n",
       "      <td>0.546531</td>\n",
       "      <td>0.528298</td>\n",
       "      <td>13892.926927</td>\n",
       "      <td>592.941349</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">linear_hidden_size</th>\n",
       "      <th>70</th>\n",
       "      <td>0.000756</td>\n",
       "      <td>0.339763</td>\n",
       "      <td>0.006210</td>\n",
       "      <td>0.343003</td>\n",
       "      <td>0.722446</td>\n",
       "      <td>0.920721</td>\n",
       "      <td>14412.286505</td>\n",
       "      <td>560.094016</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.000756</td>\n",
       "      <td>0.261296</td>\n",
       "      <td>0.006221</td>\n",
       "      <td>0.343013</td>\n",
       "      <td>0.721966</td>\n",
       "      <td>0.920811</td>\n",
       "      <td>14402.521184</td>\n",
       "      <td>554.998171</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>0.000756</td>\n",
       "      <td>0.245166</td>\n",
       "      <td>0.006145</td>\n",
       "      <td>0.342953</td>\n",
       "      <td>0.721864</td>\n",
       "      <td>0.919820</td>\n",
       "      <td>14412.429667</td>\n",
       "      <td>510.655057</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">ARIMA</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">differencing_degree</th>\n",
       "      <th>1</th>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.191234</td>\n",
       "      <td>0.078602</td>\n",
       "      <td>0.050505</td>\n",
       "      <td>0.751726</td>\n",
       "      <td>0.827677</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1157.285638</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.176774</td>\n",
       "      <td>0.059485</td>\n",
       "      <td>0.080808</td>\n",
       "      <td>0.700119</td>\n",
       "      <td>0.704654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>802.118671</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.198541</td>\n",
       "      <td>0.069568</td>\n",
       "      <td>0.050505</td>\n",
       "      <td>0.696270</td>\n",
       "      <td>0.756460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1086.715642</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                           PR_AUC            \\\n",
       "                                                              min      mean   \n",
       "algorithm  optim_param_name           optim_param_value                       \n",
       "PST        sim                        simn               0.000050  0.338418   \n",
       "                                      simo               0.000050  0.339220   \n",
       "NumentaHTM synPermInactiveDec         0.01               0.000500  0.100046   \n",
       "                                      0.005              0.000500  0.097278   \n",
       "                                      0.001              0.000500  0.097582   \n",
       "           synPermConnected           0.1                0.000500  0.097278   \n",
       "                                      0.2                0.000500  0.100681   \n",
       "                                      0.05                    NaN       NaN   \n",
       "           synPermActiveInc           0.05               0.000500  0.103190   \n",
       "                                      0.1                0.000500  0.097278   \n",
       "                                      0.2                     NaN       NaN   \n",
       "           potentialPct               0.1                0.000500  0.076253   \n",
       "                                      0.5                0.000500  0.097278   \n",
       "                                      0.9                0.000500  0.079891   \n",
       "           permanenceInc              0.05               0.000500  0.100419   \n",
       "                                      0.1                0.000500  0.097278   \n",
       "                                      0.2                0.000500  0.095074   \n",
       "           permanenceDec              0.1                0.000500  0.097278   \n",
       "                                      0.2                0.000500  0.096756   \n",
       "                                      0.05               0.000500  0.096898   \n",
       "           pamLength                  1                  0.000500  0.097278   \n",
       "                                      5                  0.000500  0.095681   \n",
       "                                      3                  0.000500  0.097651   \n",
       "           numActiveColumnsPerInhArea 50.0               0.000500  0.101024   \n",
       "                                      30.0               0.000500  0.106062   \n",
       "                                      40.0               0.000500  0.097278   \n",
       "           newSynapseCount            15.0               0.000500  0.097521   \n",
       "                                      20.0               0.000500  0.097278   \n",
       "                                      30.0               0.000500  0.096892   \n",
       "           minThreshold               9.0                0.000500  0.097278   \n",
       "                                      6.0                0.000500  0.097300   \n",
       "                                      12.0               0.000500  0.095512   \n",
       "           maxSynapsesPerSegment      32.0               0.000500  0.097278   \n",
       "                                      64.0               0.000500  0.097227   \n",
       "                                      16.0                    NaN       NaN   \n",
       "           maxSegmentsPerCell         64.0               0.000500  0.097278   \n",
       "                                      128.0              0.000500  0.097278   \n",
       "                                      256.0              0.000500  0.097278   \n",
       "           maxAge                     0                  0.000500  0.097278   \n",
       "                                      5                       NaN       NaN   \n",
       "                                      10                      NaN       NaN   \n",
       "           inputWidth                 1024.0             0.000500  0.097278   \n",
       "                                      2048.0             0.000500  0.097278   \n",
       "                                      4096.0             0.000500  0.097278   \n",
       "           initialPerm                0.15               0.000500  0.098333   \n",
       "                                      0.21               0.000500  0.097278   \n",
       "                                      0.3                0.000500  0.094185   \n",
       "           globalDecay                0                  0.000500  0.097278   \n",
       "                                      0.1                     NaN       NaN   \n",
       "                                      0.5                     NaN       NaN   \n",
       "           encoding_output_width      75.0               0.001772  0.103327   \n",
       "                                      50.0               0.000500  0.097278   \n",
       "                                      25.0               0.000050  0.054558   \n",
       "           encoding_input_width       21.0               0.000500  0.097278   \n",
       "                                      15.0                    NaN       NaN   \n",
       "                                      30.0                    NaN       NaN   \n",
       "           columnCount                4096.0             0.000500  0.107261   \n",
       "                                      1024.0             0.000500  0.108738   \n",
       "                                      2048.0             0.000500  0.097278   \n",
       "           cellsPerColumn             64.0               0.000500  0.097537   \n",
       "                                      16.0               0.000500  0.097297   \n",
       "                                      32.0               0.000500  0.097278   \n",
       "           autoDetectWaitRecords      25.0               0.000500  0.097278   \n",
       "                                      50.0               0.000500  0.097278   \n",
       "                                      75.0               0.000500  0.097278   \n",
       "           alpha                      0.2                0.000500  0.097278   \n",
       "                                      0.5                0.000500  0.097278   \n",
       "                                      0.8                0.000500  0.097278   \n",
       "           activationThreshold        12.0               0.000500  0.097278   \n",
       "                                      6.0                0.000500  0.090262   \n",
       "                                      24.0               0.008143  0.408512   \n",
       "DeepNAP    rnn_hidden_size            200                0.000756  0.237885   \n",
       "                                      140                0.000756  0.159084   \n",
       "                                      260                0.000756  0.240000   \n",
       "           partial_sequence_length    10                 0.000766  0.255736   \n",
       "                                      1                  0.000054  0.474652   \n",
       "                                      5                  0.000762  0.234055   \n",
       "                                      3                  0.000754  0.328702   \n",
       "           lstm_layers                3                  0.000755  0.296958   \n",
       "                                      1                  0.000755  0.116156   \n",
       "                                      10                      NaN       NaN   \n",
       "           linear_hidden_size         70                 0.000756  0.339763   \n",
       "                                      100                0.000756  0.261296   \n",
       "                                      130                0.000756  0.245166   \n",
       "ARIMA      differencing_degree        1                  0.000132  0.191234   \n",
       "                                      2                  0.000104  0.176774   \n",
       "                                      0                  0.000064  0.198541   \n",
       "\n",
       "                                                                    ROC_AUC  \\\n",
       "                                                           median       min   \n",
       "algorithm  optim_param_name           optim_param_value                       \n",
       "PST        sim                        simn               0.168211  0.016049   \n",
       "                                      simo               0.168737  0.016107   \n",
       "NumentaHTM synPermInactiveDec         0.01               0.046329  0.484697   \n",
       "                                      0.005              0.036126  0.489009   \n",
       "                                      0.001              0.030826  0.490909   \n",
       "           synPermConnected           0.1                0.036126  0.489009   \n",
       "                                      0.2                0.032129  0.488384   \n",
       "                                      0.05                    NaN       NaN   \n",
       "           synPermActiveInc           0.05               0.036100  0.487967   \n",
       "                                      0.1                0.036126  0.489009   \n",
       "                                      0.2                     NaN       NaN   \n",
       "           potentialPct               0.1                0.036857  0.475354   \n",
       "                                      0.5                0.036126  0.489009   \n",
       "                                      0.9                0.012154  0.487576   \n",
       "           permanenceInc              0.05               0.034597  0.489518   \n",
       "                                      0.1                0.036126  0.489009   \n",
       "                                      0.2                0.037637  0.488905   \n",
       "           permanenceDec              0.1                0.036126  0.489009   \n",
       "                                      0.2                0.037384  0.480306   \n",
       "                                      0.05               0.037147  0.489061   \n",
       "           pamLength                  1                  0.036126  0.489009   \n",
       "                                      5                  0.033515  0.488368   \n",
       "                                      3                  0.035734  0.487602   \n",
       "           numActiveColumnsPerInhArea 50.0               0.046308  0.488905   \n",
       "                                      30.0               0.043342  0.487755   \n",
       "                                      40.0               0.036126  0.489009   \n",
       "           newSynapseCount            15.0               0.044778  0.481869   \n",
       "                                      20.0               0.036126  0.489009   \n",
       "                                      30.0               0.034156  0.489061   \n",
       "           minThreshold               9.0                0.036126  0.489009   \n",
       "                                      6.0                0.034156  0.487347   \n",
       "                                      12.0               0.038428  0.485051   \n",
       "           maxSynapsesPerSegment      32.0               0.036126  0.489009   \n",
       "                                      64.0               0.036126  0.489009   \n",
       "                                      16.0                    NaN       NaN   \n",
       "           maxSegmentsPerCell         64.0               0.036126  0.489009   \n",
       "                                      128.0              0.036126  0.489009   \n",
       "                                      256.0              0.036126  0.489009   \n",
       "           maxAge                     0                  0.036126  0.489009   \n",
       "                                      5                       NaN       NaN   \n",
       "                                      10                      NaN       NaN   \n",
       "           inputWidth                 1024.0             0.036126  0.489009   \n",
       "                                      2048.0             0.036126  0.489009   \n",
       "                                      4096.0             0.036126  0.489009   \n",
       "           initialPerm                0.15               0.035506  0.489193   \n",
       "                                      0.21               0.036126  0.489009   \n",
       "                                      0.3                0.033943  0.488853   \n",
       "           globalDecay                0                  0.036126  0.489009   \n",
       "                                      0.1                     NaN       NaN   \n",
       "                                      0.5                     NaN       NaN   \n",
       "           encoding_output_width      75.0               0.048797  0.460202   \n",
       "                                      50.0               0.036126  0.489009   \n",
       "                                      25.0               0.005000  0.498232   \n",
       "           encoding_input_width       21.0               0.036126  0.489009   \n",
       "                                      15.0                    NaN       NaN   \n",
       "                                      30.0                    NaN       NaN   \n",
       "           columnCount                4096.0             0.042018  0.487828   \n",
       "                                      1024.0             0.048901  0.485606   \n",
       "                                      2048.0             0.036126  0.489009   \n",
       "           cellsPerColumn             64.0               0.038532  0.483214   \n",
       "                                      16.0               0.036048  0.482959   \n",
       "                                      32.0               0.036126  0.489009   \n",
       "           autoDetectWaitRecords      25.0               0.036126  0.489009   \n",
       "                                      50.0               0.036126  0.489009   \n",
       "                                      75.0               0.036126  0.489009   \n",
       "           alpha                      0.2                0.036126  0.489009   \n",
       "                                      0.5                0.036126  0.489009   \n",
       "                                      0.8                0.036126  0.489009   \n",
       "           activationThreshold        12.0               0.036126  0.489009   \n",
       "                                      6.0                0.033301  0.489374   \n",
       "                                      24.0               0.500058  0.050546   \n",
       "DeepNAP    rnn_hidden_size            200                0.006221  0.343013   \n",
       "                                      140                0.013636  0.342973   \n",
       "                                      260                0.006143  0.342973   \n",
       "           partial_sequence_length    10                 0.006954  0.347978   \n",
       "                                      1                  0.250000  0.078508   \n",
       "                                      5                  0.006361  0.344595   \n",
       "                                      3                  0.006221  0.343013   \n",
       "           lstm_layers                3                  0.098311  0.342923   \n",
       "                                      1                  0.024373  0.000532   \n",
       "                                      10                      NaN       NaN   \n",
       "           linear_hidden_size         70                 0.006210  0.343003   \n",
       "                                      100                0.006221  0.343013   \n",
       "                                      130                0.006145  0.342953   \n",
       "ARIMA      differencing_degree        1                  0.078602  0.050505   \n",
       "                                      2                  0.059485  0.080808   \n",
       "                                      0                  0.069568  0.050505   \n",
       "\n",
       "                                                                             \\\n",
       "                                                             mean    median   \n",
       "algorithm  optim_param_name           optim_param_value                       \n",
       "PST        sim                        simn               0.796201  0.858833   \n",
       "                                      simo               0.793667  0.860587   \n",
       "NumentaHTM synPermInactiveDec         0.01               0.568568  0.505253   \n",
       "                                      0.005              0.564347  0.503068   \n",
       "                                      0.001              0.562129  0.499016   \n",
       "           synPermConnected           0.1                0.564347  0.503068   \n",
       "                                      0.2                0.564063  0.502727   \n",
       "                                      0.05                    NaN       NaN   \n",
       "           synPermActiveInc           0.05               0.566906  0.505730   \n",
       "                                      0.1                0.564347  0.503068   \n",
       "                                      0.2                     NaN       NaN   \n",
       "           potentialPct               0.1                0.575691  0.511529   \n",
       "                                      0.5                0.564347  0.503068   \n",
       "                                      0.9                0.542880  0.499848   \n",
       "           permanenceInc              0.05               0.565133  0.503770   \n",
       "                                      0.1                0.564347  0.503068   \n",
       "                                      0.2                0.563908  0.503042   \n",
       "           permanenceDec              0.1                0.564347  0.503068   \n",
       "                                      0.2                0.564283  0.502494   \n",
       "                                      0.05               0.564216  0.502026   \n",
       "           pamLength                  1                  0.564347  0.503068   \n",
       "                                      5                  0.564174  0.502260   \n",
       "                                      3                  0.564159  0.505470   \n",
       "           numActiveColumnsPerInhArea 50.0               0.569889  0.507470   \n",
       "                                      30.0               0.566368  0.505715   \n",
       "                                      40.0               0.564347  0.503068   \n",
       "           newSynapseCount            15.0               0.567878  0.507638   \n",
       "                                      20.0               0.564347  0.503068   \n",
       "                                      30.0               0.563534  0.500584   \n",
       "           minThreshold               9.0                0.564347  0.503068   \n",
       "                                      6.0                0.564289  0.503147   \n",
       "                                      12.0               0.564175  0.502494   \n",
       "           maxSynapsesPerSegment      32.0               0.564347  0.503068   \n",
       "                                      64.0               0.564318  0.502850   \n",
       "                                      16.0                    NaN       NaN   \n",
       "           maxSegmentsPerCell         64.0               0.564347  0.503068   \n",
       "                                      128.0              0.564347  0.503068   \n",
       "                                      256.0              0.564347  0.503068   \n",
       "           maxAge                     0                  0.564347  0.503068   \n",
       "                                      5                       NaN       NaN   \n",
       "                                      10                      NaN       NaN   \n",
       "           inputWidth                 1024.0             0.564347  0.503068   \n",
       "                                      2048.0             0.564347  0.503068   \n",
       "                                      4096.0             0.564347  0.503068   \n",
       "           initialPerm                0.15               0.564573  0.505337   \n",
       "                                      0.21               0.564347  0.503068   \n",
       "                                      0.3                0.563277  0.501770   \n",
       "           globalDecay                0                  0.564347  0.503068   \n",
       "                                      0.1                     NaN       NaN   \n",
       "                                      0.5                     NaN       NaN   \n",
       "           encoding_output_width      75.0               0.590365  0.523820   \n",
       "                                      50.0               0.564347  0.503068   \n",
       "                                      25.0               0.507109  0.499840   \n",
       "           encoding_input_width       21.0               0.564347  0.503068   \n",
       "                                      15.0                    NaN       NaN   \n",
       "                                      30.0                    NaN       NaN   \n",
       "           columnCount                4096.0             0.572741  0.505458   \n",
       "                                      1024.0             0.567435  0.505431   \n",
       "                                      2048.0             0.564347  0.503068   \n",
       "           cellsPerColumn             64.0               0.564593  0.504269   \n",
       "                                      16.0               0.564435  0.504269   \n",
       "                                      32.0               0.564347  0.503068   \n",
       "           autoDetectWaitRecords      25.0               0.564347  0.503068   \n",
       "                                      50.0               0.564347  0.503068   \n",
       "                                      75.0               0.564347  0.503068   \n",
       "           alpha                      0.2                0.564347  0.503068   \n",
       "                                      0.5                0.564347  0.503068   \n",
       "                                      0.8                0.564347  0.503068   \n",
       "           activationThreshold        12.0               0.564347  0.503068   \n",
       "                                      6.0                0.559400  0.500006   \n",
       "                                      24.0               0.497997  0.500000   \n",
       "DeepNAP    rnn_hidden_size            200                0.721780  0.920811   \n",
       "                                      140                0.721554  0.798259   \n",
       "                                      260                0.721554  0.919830   \n",
       "           partial_sequence_length    10                 0.754539  0.930871   \n",
       "                                      1                  0.744835  0.999840   \n",
       "                                      5                  0.733564  0.922823   \n",
       "                                      3                  0.722018  0.920811   \n",
       "           lstm_layers                3                  0.672240  0.673023   \n",
       "                                      1                  0.546531  0.528298   \n",
       "                                      10                      NaN       NaN   \n",
       "           linear_hidden_size         70                 0.722446  0.920721   \n",
       "                                      100                0.721966  0.920811   \n",
       "                                      130                0.721864  0.919820   \n",
       "ARIMA      differencing_degree        1                  0.751726  0.827677   \n",
       "                                      2                  0.700119  0.704654   \n",
       "                                      0                  0.696270  0.756460   \n",
       "\n",
       "                                                        train_main_time  \\\n",
       "                                                                   mean   \n",
       "algorithm  optim_param_name           optim_param_value                   \n",
       "PST        sim                        simn                          NaN   \n",
       "                                      simo                          NaN   \n",
       "NumentaHTM synPermInactiveDec         0.01                          NaN   \n",
       "                                      0.005                         NaN   \n",
       "                                      0.001                         NaN   \n",
       "           synPermConnected           0.1                           NaN   \n",
       "                                      0.2                           NaN   \n",
       "                                      0.05                          NaN   \n",
       "           synPermActiveInc           0.05                          NaN   \n",
       "                                      0.1                           NaN   \n",
       "                                      0.2                           NaN   \n",
       "           potentialPct               0.1                           NaN   \n",
       "                                      0.5                           NaN   \n",
       "                                      0.9                           NaN   \n",
       "           permanenceInc              0.05                          NaN   \n",
       "                                      0.1                           NaN   \n",
       "                                      0.2                           NaN   \n",
       "           permanenceDec              0.1                           NaN   \n",
       "                                      0.2                           NaN   \n",
       "                                      0.05                          NaN   \n",
       "           pamLength                  1                             NaN   \n",
       "                                      5                             NaN   \n",
       "                                      3                             NaN   \n",
       "           numActiveColumnsPerInhArea 50.0                          NaN   \n",
       "                                      30.0                          NaN   \n",
       "                                      40.0                          NaN   \n",
       "           newSynapseCount            15.0                          NaN   \n",
       "                                      20.0                          NaN   \n",
       "                                      30.0                          NaN   \n",
       "           minThreshold               9.0                           NaN   \n",
       "                                      6.0                           NaN   \n",
       "                                      12.0                          NaN   \n",
       "           maxSynapsesPerSegment      32.0                          NaN   \n",
       "                                      64.0                          NaN   \n",
       "                                      16.0                          NaN   \n",
       "           maxSegmentsPerCell         64.0                          NaN   \n",
       "                                      128.0                         NaN   \n",
       "                                      256.0                         NaN   \n",
       "           maxAge                     0                             NaN   \n",
       "                                      5                             NaN   \n",
       "                                      10                            NaN   \n",
       "           inputWidth                 1024.0                        NaN   \n",
       "                                      2048.0                        NaN   \n",
       "                                      4096.0                        NaN   \n",
       "           initialPerm                0.15                          NaN   \n",
       "                                      0.21                          NaN   \n",
       "                                      0.3                           NaN   \n",
       "           globalDecay                0                             NaN   \n",
       "                                      0.1                           NaN   \n",
       "                                      0.5                           NaN   \n",
       "           encoding_output_width      75.0                          NaN   \n",
       "                                      50.0                          NaN   \n",
       "                                      25.0                          NaN   \n",
       "           encoding_input_width       21.0                          NaN   \n",
       "                                      15.0                          NaN   \n",
       "                                      30.0                          NaN   \n",
       "           columnCount                4096.0                        NaN   \n",
       "                                      1024.0                        NaN   \n",
       "                                      2048.0                        NaN   \n",
       "           cellsPerColumn             64.0                          NaN   \n",
       "                                      16.0                          NaN   \n",
       "                                      32.0                          NaN   \n",
       "           autoDetectWaitRecords      25.0                          NaN   \n",
       "                                      50.0                          NaN   \n",
       "                                      75.0                          NaN   \n",
       "           alpha                      0.2                           NaN   \n",
       "                                      0.5                           NaN   \n",
       "                                      0.8                           NaN   \n",
       "           activationThreshold        12.0                          NaN   \n",
       "                                      6.0                           NaN   \n",
       "                                      24.0                          NaN   \n",
       "DeepNAP    rnn_hidden_size            200                  14387.459396   \n",
       "                                      140                  14049.248946   \n",
       "                                      260                  14134.171681   \n",
       "           partial_sequence_length    10                   14400.246819   \n",
       "                                      1                     6917.062416   \n",
       "                                      5                    14411.838211   \n",
       "                                      3                    14411.976403   \n",
       "           lstm_layers                3                    14411.467708   \n",
       "                                      1                    13892.926927   \n",
       "                                      10                            NaN   \n",
       "           linear_hidden_size         70                   14412.286505   \n",
       "                                      100                  14402.521184   \n",
       "                                      130                  14412.429667   \n",
       "ARIMA      differencing_degree        1                             NaN   \n",
       "                                      2                             NaN   \n",
       "                                      0                             NaN   \n",
       "\n",
       "                                                        execute_main_time  \\\n",
       "                                                                     mean   \n",
       "algorithm  optim_param_name           optim_param_value                     \n",
       "PST        sim                        simn                      22.581874   \n",
       "                                      simo                      22.927654   \n",
       "NumentaHTM synPermInactiveDec         0.01                      94.840149   \n",
       "                                      0.005                     95.031569   \n",
       "                                      0.001                     94.519050   \n",
       "           synPermConnected           0.1                       94.028571   \n",
       "                                      0.2                       96.036952   \n",
       "                                      0.05                            NaN   \n",
       "           synPermActiveInc           0.05                      97.191249   \n",
       "                                      0.1                       95.382094   \n",
       "                                      0.2                             NaN   \n",
       "           potentialPct               0.1                       93.048296   \n",
       "                                      0.5                       96.349593   \n",
       "                                      0.9                       97.564084   \n",
       "           permanenceInc              0.05                      97.821784   \n",
       "                                      0.1                       96.729472   \n",
       "                                      0.2                       97.432688   \n",
       "           permanenceDec              0.1                       98.088535   \n",
       "                                      0.2                       97.988004   \n",
       "                                      0.05                      97.435965   \n",
       "           pamLength                  1                         98.426819   \n",
       "                                      5                         98.228420   \n",
       "                                      3                         99.492750   \n",
       "           numActiveColumnsPerInhArea 50.0                     101.529713   \n",
       "                                      30.0                      94.781598   \n",
       "                                      40.0                      97.968474   \n",
       "           newSynapseCount            15.0                      98.198422   \n",
       "                                      20.0                      97.814221   \n",
       "                                      30.0                     100.618047   \n",
       "           minThreshold               9.0                       97.170242   \n",
       "                                      6.0                       97.513560   \n",
       "                                      12.0                      97.310443   \n",
       "           maxSynapsesPerSegment      32.0                      97.858673   \n",
       "                                      64.0                      98.596435   \n",
       "                                      16.0                            NaN   \n",
       "           maxSegmentsPerCell         64.0                      95.673058   \n",
       "                                      128.0                     96.805896   \n",
       "                                      256.0                     96.019019   \n",
       "           maxAge                     0                         94.659237   \n",
       "                                      5                               NaN   \n",
       "                                      10                              NaN   \n",
       "           inputWidth                 1024.0                    92.791690   \n",
       "                                      2048.0                    92.808960   \n",
       "                                      4096.0                    94.313934   \n",
       "           initialPerm                0.15                      94.422149   \n",
       "                                      0.21                      93.292459   \n",
       "                                      0.3                       94.953588   \n",
       "           globalDecay                0                         87.618928   \n",
       "                                      0.1                             NaN   \n",
       "                                      0.5                             NaN   \n",
       "           encoding_output_width      75.0                      95.298736   \n",
       "                                      50.0                      88.496017   \n",
       "                                      25.0                      86.232022   \n",
       "           encoding_input_width       21.0                      88.511325   \n",
       "                                      15.0                            NaN   \n",
       "                                      30.0                            NaN   \n",
       "           columnCount                4096.0                   138.440881   \n",
       "                                      1024.0                    66.900552   \n",
       "                                      2048.0                    90.728706   \n",
       "           cellsPerColumn             64.0                     125.271610   \n",
       "                                      16.0                      72.976719   \n",
       "                                      32.0                      91.415208   \n",
       "           autoDetectWaitRecords      25.0                      92.161906   \n",
       "                                      50.0                      91.497845   \n",
       "                                      75.0                      92.068992   \n",
       "           alpha                      0.2                       87.821727   \n",
       "                                      0.5                       87.642060   \n",
       "                                      0.8                       87.947527   \n",
       "           activationThreshold        12.0                      92.085590   \n",
       "                                      6.0                       96.112704   \n",
       "                                      24.0                      93.860859   \n",
       "DeepNAP    rnn_hidden_size            200                      552.284223   \n",
       "                                      140                     1088.230355   \n",
       "                                      260                      563.656898   \n",
       "           partial_sequence_length    10                       559.587427   \n",
       "                                      1                        208.561622   \n",
       "                                      5                        640.338980   \n",
       "                                      3                        554.589953   \n",
       "           lstm_layers                3                       1228.321475   \n",
       "                                      1                        592.941349   \n",
       "                                      10                              NaN   \n",
       "           linear_hidden_size         70                       560.094016   \n",
       "                                      100                      554.998171   \n",
       "                                      130                      510.655057   \n",
       "ARIMA      differencing_degree        1                       1157.285638   \n",
       "                                      2                        802.118671   \n",
       "                                      0                       1086.715642   \n",
       "\n",
       "                                                        repetition  \n",
       "                                                             count  \n",
       "algorithm  optim_param_name           optim_param_value             \n",
       "PST        sim                        simn                     168  \n",
       "                                      simo                     168  \n",
       "NumentaHTM synPermInactiveDec         0.01                     168  \n",
       "                                      0.005                    168  \n",
       "                                      0.001                    168  \n",
       "           synPermConnected           0.1                      168  \n",
       "                                      0.2                      168  \n",
       "                                      0.05                     168  \n",
       "           synPermActiveInc           0.05                     168  \n",
       "                                      0.1                      168  \n",
       "                                      0.2                      168  \n",
       "           potentialPct               0.1                      168  \n",
       "                                      0.5                      168  \n",
       "                                      0.9                      168  \n",
       "           permanenceInc              0.05                     168  \n",
       "                                      0.1                      168  \n",
       "                                      0.2                      168  \n",
       "           permanenceDec              0.1                      168  \n",
       "                                      0.2                      168  \n",
       "                                      0.05                     168  \n",
       "           pamLength                  1                        168  \n",
       "                                      5                        168  \n",
       "                                      3                        168  \n",
       "           numActiveColumnsPerInhArea 50.0                     168  \n",
       "                                      30.0                     168  \n",
       "                                      40.0                     168  \n",
       "           newSynapseCount            15.0                     168  \n",
       "                                      20.0                     168  \n",
       "                                      30.0                     168  \n",
       "           minThreshold               9.0                      168  \n",
       "                                      6.0                      168  \n",
       "                                      12.0                     168  \n",
       "           maxSynapsesPerSegment      32.0                     168  \n",
       "                                      64.0                     168  \n",
       "                                      16.0                     168  \n",
       "           maxSegmentsPerCell         64.0                     168  \n",
       "                                      128.0                    168  \n",
       "                                      256.0                    168  \n",
       "           maxAge                     0                        168  \n",
       "                                      5                        168  \n",
       "                                      10                       168  \n",
       "           inputWidth                 1024.0                   168  \n",
       "                                      2048.0                   168  \n",
       "                                      4096.0                   168  \n",
       "           initialPerm                0.15                     168  \n",
       "                                      0.21                     168  \n",
       "                                      0.3                      168  \n",
       "           globalDecay                0                        168  \n",
       "                                      0.1                      168  \n",
       "                                      0.5                      168  \n",
       "           encoding_output_width      75.0                     168  \n",
       "                                      50.0                     168  \n",
       "                                      25.0                     168  \n",
       "           encoding_input_width       21.0                     168  \n",
       "                                      15.0                     168  \n",
       "                                      30.0                     168  \n",
       "           columnCount                4096.0                   168  \n",
       "                                      1024.0                   168  \n",
       "                                      2048.0                   168  \n",
       "           cellsPerColumn             64.0                     168  \n",
       "                                      16.0                     168  \n",
       "                                      32.0                     168  \n",
       "           autoDetectWaitRecords      25.0                     168  \n",
       "                                      50.0                     168  \n",
       "                                      75.0                     168  \n",
       "           alpha                      0.2                      168  \n",
       "                                      0.5                      168  \n",
       "                                      0.8                      168  \n",
       "           activationThreshold        12.0                     168  \n",
       "                                      6.0                      168  \n",
       "                                      24.0                     168  \n",
       "DeepNAP    rnn_hidden_size            200                      193  \n",
       "                                      140                      193  \n",
       "                                      260                      193  \n",
       "           partial_sequence_length    10                       193  \n",
       "                                      1                        193  \n",
       "                                      5                        193  \n",
       "                                      3                        193  \n",
       "           lstm_layers                3                        193  \n",
       "                                      1                        193  \n",
       "                                      10                       193  \n",
       "           linear_hidden_size         70                       193  \n",
       "                                      100                      193  \n",
       "                                      130                      193  \n",
       "ARIMA      differencing_degree        1                        168  \n",
       "                                      2                        168  \n",
       "                                      0                        168  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_by = (\"ROC_AUC\", \"mean\")\n",
    "metric_agg_type = [\"min\", \"mean\", \"median\"]\n",
    "time_agg_type = \"mean\"\n",
    "aggs = {\n",
    "    \"PR_AUC\": metric_agg_type,\n",
    "    \"ROC_AUC\": metric_agg_type,\n",
    "    \"train_main_time\": time_agg_type,\n",
    "    \"execute_main_time\": time_agg_type,\n",
    "    \"repetition\": \"count\"\n",
    "}\n",
    "\n",
    "df_tmp = df.reset_index()\n",
    "df_tmp = df_tmp.groupby(by=[\"algorithm\", \"optim_param_name\", \"optim_param_value\"]).agg(aggs)\n",
    "df_tmp = df_tmp.reset_index()\n",
    "df_tmp = df_tmp.sort_values(by=[\"algorithm\", \"optim_param_name\", sort_by], ascending=False)\n",
    "df_tmp = df_tmp.set_index([\"algorithm\", \"optim_param_name\", \"optim_param_value\"])\n",
    "\n",
    "with pd.option_context(\"display.max_rows\", None, \"display.max_columns\", None):\n",
    "    display(df_tmp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selected parameters\n",
    "\n",
    "- PST: `sim=\"SIMn\"`\n",
    "- NumentaHTM: \n",
    "  ```json\n",
    "  \"NumentaHTM\": {\n",
    "      \"alpha\": 0.5,  # no change\n",
    "      \"globalDecay\": 0,  # only 0 works\n",
    "      \"encoding_output_width\": 75,\n",
    "      \"encoding_input_width\": 21,  # only 21 works\n",
    "      \"columnCount\": 2048,  # no significant change; lower -> faster\n",
    "      \"cellsPerColumn\": 32,  # no signifacnt change; lower -> faster\n",
    "      \"autoDetectWaitRecords\": 50,  # no chnage\n",
    "      \"activationThreshold\": 12,  # strange behavior, but no significant improvement over default\n",
    "      \"inputWidth\": 2048,  # no change\n",
    "      \"initialPerm\": 0.15,\n",
    "      \"maxAge\": 0,  # only 0 works\n",
    "      \"synPermConnected\": 0.1,  # only 0.1 and 0.2 works\n",
    "      \"synPermInactiveDec\": 0.01,\n",
    "      \"synPermActiveInc\": 0.05,\n",
    "      \"maxSegmentsPerCell\": 128,  # no change\n",
    "      \"potentialPct\": 0.1,\n",
    "      \"permanenceInc\": 0.1,  # no significant change\n",
    "      \"permanenceDec\": 0.1,  # no significant change\n",
    "      \"pamLength\": 1,  # no significant change\n",
    "      \"numActiveColumnsPerInhArea\": 50,\n",
    "      \"newSynapseCount\": 15,\n",
    "      \"minThreshold\": 9,  # no significant change\n",
    "      \"maxSynapsesPerSegment\": 32  # only 32 and 64 works; no significant change\n",
    "  }\n",
    "  ```\n",
    "- DeepNAP:\n",
    "  ```json\n",
    "  \"DeepNAP\": {\n",
    "      \"rnn_hidden_size\": 200,\n",
    "      \"linear_hidden_size\": 100,\n",
    "      \"partial_sequency_length\": 1,\n",
    "      \"lstm_layers\": 1\n",
    "  }\n",
    "  ```\n",
    "  (lstm_layers: more are better, but quite a lot slower; linear_hidden_size: almost no difference, using default; partial_sequence_length: 10 is slightly better in mean ROC_AUC, but 1 is better in all other metrics and even faster)\n",
    "- ARIMA: `differencing_degree=1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores([\n",
    "    (\"NumentaHTM\", \"activationThreshold\", 6),\n",
    "    (\"NumentaHTM\", \"activationThreshold\", 12),\n",
    "    (\"NumentaHTM\", \"activationThreshold\", 24)\n",
    "], \"sinus-diff-count-5\", use_plotly=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Failed runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>repetition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>algorithm</th>\n",
       "      <th>optim_param_name</th>\n",
       "      <th>optim_param_value</th>\n",
       "      <th>status</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ARIMA</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">differencing_degree</th>\n",
       "      <th>1.00</th>\n",
       "      <th>Status.TIMEOUT</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"26\" valign=\"top\">DeepNAP</th>\n",
       "      <th rowspan=\"6\" valign=\"top\">linear_hidden_size</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">70.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status.TIMEOUT</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">100.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status.TIMEOUT</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">130.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status.TIMEOUT</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">lstm_layers</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status.TIMEOUT</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status.TIMEOUT</th>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status.TIMEOUT</th>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">partial_sequence_length</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status.TIMEOUT</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status.TIMEOUT</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">5.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status.TIMEOUT</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status.TIMEOUT</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">rnn_hidden_size</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">140.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status.TIMEOUT</th>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">200.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status.TIMEOUT</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">260.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status.TIMEOUT</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">NumentaHTM</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">encoding_input_width</th>\n",
       "      <th>15.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">globalDecay</th>\n",
       "      <th>0.10</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">maxAge</th>\n",
       "      <th>5.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maxSynapsesPerSegment</th>\n",
       "      <th>16.00</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synPermActiveInc</th>\n",
       "      <th>0.20</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>synPermConnected</th>\n",
       "      <th>0.05</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                     repetition\n",
       "algorithm  optim_param_name        optim_param_value status                    \n",
       "ARIMA      differencing_degree     1.00              Status.TIMEOUT           2\n",
       "                                   2.00              Status.ERROR             1\n",
       "DeepNAP    linear_hidden_size      70.00             Status.ERROR           132\n",
       "                                                     Status.TIMEOUT          56\n",
       "                                   100.00            Status.ERROR           131\n",
       "                                                     Status.TIMEOUT          57\n",
       "                                   130.00            Status.ERROR           145\n",
       "                                                     Status.TIMEOUT          43\n",
       "           lstm_layers             1.00              Status.ERROR            29\n",
       "                                                     Status.TIMEOUT          12\n",
       "                                   3.00              Status.ERROR            20\n",
       "                                                     Status.TIMEOUT         169\n",
       "                                   10.00             Status.ERROR            27\n",
       "                                                     Status.TIMEOUT         166\n",
       "           partial_sequence_length 1.00              Status.ERROR           128\n",
       "                                                     Status.TIMEOUT          48\n",
       "                                   3.00              Status.ERROR           138\n",
       "                                                     Status.TIMEOUT          50\n",
       "                                   5.00              Status.ERROR           138\n",
       "                                                     Status.TIMEOUT          50\n",
       "                                   10.00             Status.ERROR           134\n",
       "                                                     Status.TIMEOUT          54\n",
       "           rnn_hidden_size         140.00            Status.ERROR           133\n",
       "                                                     Status.TIMEOUT          52\n",
       "                                   200.00            Status.ERROR           128\n",
       "                                                     Status.TIMEOUT          60\n",
       "                                   260.00            Status.ERROR           132\n",
       "                                                     Status.TIMEOUT          56\n",
       "NumentaHTM encoding_input_width    15.00             Status.ERROR           168\n",
       "                                   30.00             Status.ERROR           168\n",
       "           globalDecay             0.10              Status.ERROR           168\n",
       "                                   0.50              Status.ERROR           168\n",
       "           maxAge                  5.00              Status.ERROR           168\n",
       "                                   10.00             Status.ERROR           168\n",
       "           maxSynapsesPerSegment   16.00             Status.ERROR           168\n",
       "           synPermActiveInc        0.20              Status.ERROR           168\n",
       "           synPermConnected        0.05              Status.ERROR           168"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"status\"] != \"Status.OK\"].groupby(by=[\"algorithm\", \"optim_param_name\", \"optim_param_value\", \"status\"])[[\"repetition\"]].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "timeeval38",
   "language": "python",
   "name": "timeeval38"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
